Abstract
In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features. We show how leveraging distributional features obtained from large in-domain unlabeled data helps to easily and quickly develop a reasonably good performing system for detecting hyperpartisan news. The system and models generated for this task are publicly available.- Anthology ID:
- S19-2161
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 944–948
- Language:
- URL:
- https://aclanthology.org/S19-2161
- DOI:
- 10.18653/v1/S19-2161
- Cite (ACL):
- Rodrigo Agerri. 2019. Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 944–948, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features (Agerri, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S19-2161.pdf